Methodics and tools of cough sound processing on basic of neural net

The purpose of the article is to analyze the methods and means of processing cough sounds to detect lung diseases, as well as to describe the developed system for classifying and detecting cough sounds based on a deep neural network. Four types of machine learning and the use of convolutional neural network (CNN) are considered. Hypermarkets of CNN are given. Varieties of machine learning based on the CNN are discussed. The analysis of works on the methodology and means of processing cough sounds based on the CNN with the reduction of the means used and the accuracy of recognition is carried out. Details of machine learning using the environmental sound classification 50 (ESC-50) dataset are discussed. To recognize COVID-19 cough, a classifier was analyzed using CNN as a machine learning model. The proposed CNN system is designed to classify and detect cough sounds based on ESC-50. After selecting a set of sound classification data, four stages are described: extraction of features from audio files, labeling, training, testing. The ESC-50 used for the study was downloaded from the Kaggle website. Python libraries and modules related to deep learning and data science were used to implement the project: NumPy, Librosa, Matplotlib, Hickle, Sci-Kit Learn, Keras. The implemented network used a stochastic gradient algorithm. Several volunteers recorded their voices while coughing using their smartphones and it was assured to record their voices in a public environment to introduce noise to the sounds, in addition to some audio files that were downloaded online. The results showed an average accuracy of 85.37 %, precision of 78.8 % and a recall record of 91.9 %

Identificación automática de neumonía mediante el procesamiento digital del sonido

En la actualidad la neumonía es la principal causa de mortalidad infantil, alcanzando anualmente a cifra 1.1 millones de fallecimiento de niños menores de 5 años. Para diagnosticar oportunamente esta enfermedad se realiza por medio de la auscultación pulmonar, empleando un estetoscopio, el cual permite percibir los sonidos respiratorios y así descubrir algún signo anormal. El objetivo de este trabajo es realizar la Identificación de manera automática la neumonía mediante el procesamiento digital del sonido, la investigación inicia con la construcción de un protocolo para la adquisición de sonidos de enfermedades respiratorias, que sirvieron para la adquisición de sonidos respiratorios, posteriormente se transformó el audio a imagen para adquirir el espectrograma de cada uno, al tener el espectrograma se realizó el procesamiento de las imágenes utilizando Keras, así mismo creamos nuestra red neuronal convolucional y comenzamos a realizar el entrenamiento con el dataset de imágenes con un valor inicial de 200 épocas. Los resultados fueron satisfactorios debido a que se obtuvo un 75%, 69% y 75% de la exactitud, precisión y sensibilidad, respectivamente. Finalmente se evaluó el método, el cual tiene un buen desempeño con respecto a la identificación automática de la neumonía.TesisInfraestructura, Tecnología y Medio Ambient

PRACTICAL COUGH DETECTION IN PRESENCE OF BACKGROUND NOISE AND PRELIMINARY DIFFERENTIAL DIAGNOSIS FROM COUGH SOUND USING ARTIFICIAL INTELLIGENCE

Cough is one of the most common symptoms for many of the diseases. Physicians have been using characteristics of cough for preliminary diagnosis of certain respiratory diseases for ages. But the methods have been subjective and often depend on self-reported history and description of cough by the patients. Recently, with advent of the omnipresent recording devices and advances in machine learning capabilities, many studies have attempted to partially fill the gap. These studies have approached the problem objectively to create devices like cough monitors, cough counters, and partial automatic cough detection using machine learning. There is still a huge gap that exists in detecting and diagnosing the cough in a practical way. This study is an attempt to contribute towards filling this gap. We propose and analyze a machine learning based method to automatically detect cough in presence of background noise. After successful cough detection, we investigate the possibility of preliminary differential diagnosis by distinguishing the cough associated with Asthma, Bronchitis, Bronchiolitis, Pertussis patients and healthy people. As more training data could be collected for cough and non-cough sounds, it allowed us to leverage the potential of powerful deep architecture like ResNet for the cough detection part. For the diagnosis part of the work, not much data was available. In this case the preliminary results show that XGBoost performed better than CNN and ResNet architectures. While the cough detection part of the study offers mature results, lot more cough sound data for the examined diseases is needed before generalizable conclusions can be drawn from the diagnosis results observed in this study

Biomechanical models of cough sounds in pneumonia: mechanisms underlying sound-based diagnosis in low-resource settings

Objective. This paper describes a theoretical study of physical mechanisms underlying the generation of cough sounds and the expected differences in such sounds caused by anatomically localized pneumonia. Methods. A fresh, first-principles, physics-based analysis is done, describing radial motion of bronchi after sudden decompression of intrathoracic pressure during the early and middle phases of a cough. The mechanical model of each bronchus is a spring-mass-damper system, in which the spring force comes from elastic properties of the bronchial wall, the mass term comes from the bronchial wall and any surrounding fluid or pus in pneumonia, and the damping term comes from the extracellular matrix within bronchial walls. Upon release of pressure built-up during the compressive phase of the cough, model bronchial walls undergo damped sinusoidal motion. Cough sound intensity is computed as the weighted average of the signed products of air density and the squares of local radial wall velocities, with weighting factors determined by the aggregate inner surface areas of bronchi at each segmental level. Established anatomic and physiologic data, coupled with classical scaling rules, are used to create adult and child sized models with or without pneumonia. Digital signal processing is done to separate the hidden pneumonia-related signal from raw cough sound data. Results. Numerical computations generate simulated cough sounds of realistic amplitudes, durations, and frequency content. Large airways generate loud sounds, and small airways generate much softer sounds. Cough sounds generated by medium and small airways that are surrounded by pneumonic fluid have lower frequency and longer duration. Simple low pass filtering separates the fainter, prolonged, pneumonia-related sounds from louder, earlier sounds generated in the trachea and main stem bronchi. The ratio of the root mean square (RMS) low-pass filtered sound pressure to the RMS raw sound pressure, plotted in the time domain, provides excellent discrimination of pneumonia models from normal ones. Involvement of as little as 10% of the total lung tissue with pneumonic fluid infiltrate yields a 4-fold difference in the RMS power ratio. Conclusions. This work demonstrates a heretofore unrecognized mechanism underlying cough-sound based recognition of pneumonia cases that is anatomically, physiologically, and physically realistic, strengthening the rationale for a low cost, easy-to-use, completely painless, and cell-phone-based diagnostic tool for childhood pneumonia in remote, low-resource settings

Building a low-cost biomedical device to improve accuracy in pneumonia diagnosis in under five children.

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, April 2019Pneumonia has been the leading cause of death among children under the age of five in sub-Saharan Africa, killing more children than the number of children dying from HIV/AIDS. The current methods of diagnosing pneumonia are limited by poor sensitivity and accuracy and they are also expensive. In this project, a low-cost biomedical device was designed and developed to improve the accuracy in diagnosing pneumonia hence assisting in correct prescription of drugs to children. Sounds waves were transmitted from a surface exciter which was connected to an Arduino-powered circuit. The sounds waves were allowed to pass through one side of a lung phantom made of sponge and were detected on the other side using an electronic stethoscope. 4 dry sponges and four wet sponges were used to represent a healthy lung and a pneumonia consolidated lung respectively. The sound signals detected by the electronic stethoscope were analyzed using the Digital Signal Processing toolboxes in Audacity and MATLAB software. The difference in the resonant frequencies when the sound waves traveled through the dry and wet sponges was used to diagnose pneumonia. The device uses a non-invasive method which does not cause any health defects, unlike the chest x-ray method which can cause cancer due to its use of electromagnetic radiation to diagnose pneumonia. The results were then discussed for the design and application in pneumonia diagnosis in infantsAshesi Universit

Cough Monitoring Through Audio Analysis

The detection of cough events in audio recordings requires the analysis of a significant amount of data as cough is typically monitored continuously over several hours to capture naturally occurring cough events. The recorded data is mostly composed of undesired sound events such as silence, background noise, and speech. To reduce computational costs and to address the ethical concerns raised from the collection of audio data in public environments, the data requires pre-processing prior to any further analysis. Current cough detection algorithms typically use pre-processing methods to remove undesired audio segments from the collected data but do not preserve the privacy of individuals being recorded while monitoring respiratory events. This study reveals the need for an automatic pre-processing method that removes sensitive data from the recording prior to any further analysis to ensure privacy preservation of individuals. Specific characteristics of cough sounds can be used to discard sensitive data from audio recordings at a pre-processing stage, improving privacy preservation, and decreasing ethical concerns when dealing with cough monitoring through audio analysis. We propose a pre-processing algorithm that increases privacy preservation and significantly decreases the amount of data to be analysed, by separating cough segments from other non-cough segments, including speech, in audio recordings. Our method verifies the presence of signal energy in both lower and higher frequency regions and discards segments whose energy concentrates only on one of them. The method is iteratively applied on the same data to increase the percentage of data reduction and privacy preservation. We evaluated the performance of our algorithm using several hours of audio recordings with manually pre-annotated cough and speech events. Our results showed that 5 iterations of the proposed method can discard up to 88.94% of the speech content present in the recordings, allowing for a strong privacy preservation while considerably reducing the amount of data to be further analysed by 91.79%. The data reduction and privacy preservation achievements of the proposed pre-processing algorithm offers the possibility to use larger datasets captured in public environments and would beneficiate all cough detection algorithms by preserving the privacy of subjects and by-stander conversations recorded during cough monitoring

Current diagnostic techniques for pneumonia: a scoping review

Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients